Bayescenv confirm convergence
Devon DeRaad
8/26/2020
library(coda)## Warning: package 'coda' was built under R version 3.5.2library(lattice)2R hum
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.6227
## Fst2 passed 1 0.9859
## Fst3 passed 1 0.7856
## Fst4 passed 1 0.3505
## Fst5 passed 1 0.8749
## Fst6 passed 1 0.0843
## Fst7 passed 1 0.1021
## Fst8 passed 1 0.9269
## Fst9 passed 1 0.1180
## Fst10 passed 1 0.5531
## Fst11 passed 1 0.7365
## Fst12 passed 2001 0.1756
## Fst13 passed 1 0.2443
## Fst14 passed 1 0.9299
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0603 6.67e-06
## Fst2 passed 0.0241 4.27e-06
## Fst3 passed 0.0364 5.00e-06
## Fst4 passed 0.0294 3.95e-06
## Fst5 passed 0.0358 5.96e-06
## Fst6 passed 0.0300 5.37e-06
## Fst7 passed 0.0432 4.50e-06
## Fst8 passed 0.0569 5.18e-06
## Fst9 passed 0.0208 3.02e-06
## Fst10 passed 0.0208 3.02e-06
## Fst11 passed 0.0622 6.16e-06
## Fst12 passed 0.4568 3.17e-05
## Fst13 passed 0.1145 1.08e-05
## Fst14 passed 0.0249 3.48e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3271.006 2488.018 2856.687 3655.435 4303.057 4209.196 3968.223 3708.013
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3101.758 2979.385 2951.396 4607.163 3812.188 3486.062plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.168784606 0.255240332 0.227968359 0.1551481489 0.074716539
## Lag 50 -0.007916957 0.037564529 0.010855904 -0.0171478564 0.003740783
## Lag 100 0.012401647 -0.006469221 -0.005723599 -0.0121210717 0.022491267
## Lag 500 -0.015879618 0.002322632 0.027177250 -0.0006427306 -0.009042370
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.0000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.08567255 0.1148507177 0.14817192 0.214893448 0.20845917
## Lag 50 0.01154717 0.0070821780 0.00930051 0.005278132 0.03048233
## Lag 100 -0.01490612 -0.0086222395 -0.01589813 0.001851678 -0.01765533
## Lag 500 0.01558492 -0.0007983081 0.02672486 0.013587178 -0.01070272
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.19641526 0.04069025 0.104253819 0.178209216
## Lag 50 0.00173944 0.02478804 -0.004895669 0.011650920
## Lag 100 0.01112094 0.01683631 -0.003520598 -0.020390577
## Lag 500 0.01523973 0.01157483 -0.010172876 0.000879831levelplot(t(autocorr.diag(chain)))
plot(chain)
2L hum
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.0551
## Fst2 passed 1 0.2781
## Fst3 passed 1 0.1969
## Fst4 passed 1 0.0606
## Fst5 passed 1 0.4148
## Fst6 passed 1 0.1648
## Fst7 passed 1 0.9944
## Fst8 passed 1 0.0991
## Fst9 passed 1 0.9120
## Fst10 passed 1 0.8679
## Fst11 passed 1 0.6065
## Fst12 passed 1 0.4698
## Fst13 passed 1 0.1486
## Fst14 passed 1 0.7841
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.1228 1.42e-05
## Fst2 passed 0.0931 1.08e-05
## Fst3 passed 0.0917 1.13e-05
## Fst4 passed 0.0375 5.96e-06
## Fst5 passed 0.0267 6.94e-06
## Fst6 passed 0.0303 7.53e-06
## Fst7 passed 0.0334 5.30e-06
## Fst8 passed 0.0580 6.48e-06
## Fst9 passed 0.0145 3.50e-06
## Fst10 passed 0.0421 6.63e-06
## Fst11 passed 0.0799 8.56e-06
## Fst12 passed 0.4373 3.10e-05
## Fst13 passed 0.1574 1.61e-05
## Fst14 passed 0.0366 6.35e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3319.051 3508.119 3165.469 3398.867 3783.645 3284.620 3249.030 3663.860
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2425.672 2681.358 3445.515 4370.792 3894.237 2722.635plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000 1.00000000
## Lag 10 0.174564536 0.158310388 0.1841596014 0.168331716 0.10668348
## Lag 50 0.006351562 0.008179829 0.0140239275 0.020503993 -0.01742444
## Lag 100 0.007170402 0.003311487 -0.0021648969 -0.002499203 -0.00081555
## Lag 500 0.016639670 -0.011883007 -0.0004225282 0.022055519 -0.02012081
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.0000000000
## Lag 10 0.141457806 0.169048215 0.133315654 0.298429353 0.2586503779
## Lag 50 0.005323411 0.013989568 0.018976475 0.001915693 0.0003659035
## Lag 100 0.014505671 0.004983617 0.019248397 0.014203885 0.0033529302
## Lag 500 0.001492427 0.002319752 -0.003511402 -0.002219178 -0.0313196795
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000
## Lag 10 0.183867154 0.066946498 0.124126574 0.25367759
## Lag 50 0.001368993 0.008825577 -0.005861448 0.01390425
## Lag 100 0.013602668 -0.012625400 -0.011076151 -0.01628005
## Lag 500 -0.005401785 0.003347106 -0.010215184 -0.01558191levelplot(t(autocorr.diag(chain)))
plot(chain)
3R hum
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.314
## Fst2 passed 1 0.551
## Fst3 passed 1 0.987
## Fst4 passed 1 0.842
## Fst5 passed 1 0.556
## Fst6 passed 1 0.508
## Fst7 passed 1 0.180
## Fst8 passed 1 0.146
## Fst9 passed 1 0.626
## Fst10 passed 1 0.305
## Fst11 passed 1 0.875
## Fst12 passed 1 0.371
## Fst13 passed 1 0.822
## Fst14 passed 1 0.955
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02045 3.79e-06
## Fst2 passed 0.00516 2.03e-06
## Fst3 passed 0.00889 2.32e-06
## Fst4 passed 0.01768 2.92e-06
## Fst5 passed 0.02267 5.25e-06
## Fst6 passed 0.02112 4.77e-06
## Fst7 passed 0.01783 2.97e-06
## Fst8 passed 0.05939 5.34e-06
## Fst9 passed 0.01155 2.41e-06
## Fst10 passed 0.01045 1.97e-06
## Fst11 passed 0.03979 4.44e-06
## Fst12 passed 0.26592 2.38e-05
## Fst13 passed 0.08136 8.55e-06
## Fst14 passed 0.01630 2.73e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2923.514 2799.283 2870.184 4021.534 3917.821 4217.814 3288.256 4040.331
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2577.361 2960.573 3434.629 4004.605 4124.690 3594.867plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.00000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.232074738 0.21373674 0.221548039 0.108261194 0.082143906
## Lag 50 0.025223779 0.02346283 0.008461970 -0.001510485 0.017676703
## Lag 100 -0.009140293 0.01248846 -0.002674830 -0.005365285 -0.004040461
## Lag 500 -0.000246087 -0.01604042 -0.004852505 0.038041243 0.001206352
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.084657304 0.1462077889 0.105956390 0.207964306 0.207403650
## Lag 50 -0.008843852 0.0193159532 0.007586496 0.042110680 0.001357519
## Lag 100 0.029043640 0.0003761396 -0.019714060 0.005080454 -0.025156367
## Lag 500 0.014482301 0.0037913387 0.002370540 -0.001676296 0.010382988
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.144757323 0.0753701719 0.075541177 0.135209292
## Lag 50 -0.002783186 0.0245059081 -0.003630617 -0.007007132
## Lag 100 -0.004061852 -0.0071578618 0.021155032 -0.007259280
## Lag 500 -0.017582142 -0.0003327718 0.019381872 -0.006433374levelplot(t(autocorr.diag(chain)))
plot(chain)
3L hum
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.5413
## Fst2 passed 1 0.9092
## Fst3 passed 501 0.1222
## Fst4 passed 1 0.8131
## Fst5 passed 1001 0.0998
## Fst6 passed 1 0.4163
## Fst7 passed 1 0.7344
## Fst8 passed 1 0.3577
## Fst9 failed NA 0.0211
## Fst10 passed 1 0.0774
## Fst11 passed 1 0.3875
## Fst12 passed 1 0.9676
## Fst13 passed 1 0.6234
## Fst14 passed 1 0.9466
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02242 4.56e-06
## Fst2 passed 0.00639 2.28e-06
## Fst3 passed 0.01104 3.10e-06
## Fst4 passed 0.01350 3.08e-06
## Fst5 passed 0.01487 5.61e-06
## Fst6 passed 0.01387 4.78e-06
## Fst7 passed 0.01800 3.29e-06
## Fst8 passed 0.05464 6.08e-06
## Fst9 <NA> NA NA
## Fst10 passed 0.00760 1.80e-06
## Fst11 passed 0.03774 5.19e-06
## Fst12 passed 0.25382 2.54e-05
## Fst13 passed 0.08533 1.00e-05
## Fst14 passed 0.01630 3.04e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3060.150 3036.197 2933.150 3441.250 4555.217 4320.351 3591.420 3904.154
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3090.127 3447.192 3027.765 4356.104 4280.474 3823.150plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.0000000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.1724964847 0.1707428049 0.218965215 0.143137600 0.046349081
## Lag 50 -0.0175224727 -0.0002878109 0.019876502 0.006047537 0.004111728
## Lag 100 -0.0005221605 0.0136453874 -0.004123944 0.014990245 0.020166019
## Lag 500 0.0232963192 0.0093726180 -0.003757280 -0.024932951 -0.024865619
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.072721935 0.142935212 0.122874260 0.21299296 0.183631983
## Lag 50 -0.001802276 -0.004918926 -0.011906069 0.01491150 0.017119024
## Lag 100 0.015557672 0.019538139 0.011565133 0.01315479 0.001341083
## Lag 500 0.009532110 0.023931592 -0.006159467 -0.00990996 -0.009589162
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.0000000000
## Lag 10 0.158532700 0.068621822 0.077332360 0.1026402424
## Lag 50 0.012495780 -0.004198612 0.008781246 -0.0090920412
## Lag 100 -0.008764265 0.004186153 0.022839601 -0.0079769778
## Lag 500 -0.000552086 -0.001577513 0.003687224 -0.0006713689levelplot(t(autocorr.diag(chain)))
plot(chain)
X hum
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.3292
## Fst2 passed 1 0.4690
## Fst3 passed 1 0.4131
## Fst4 passed 1 0.5568
## Fst5 passed 1 0.5745
## Fst6 passed 1 0.9732
## Fst7 passed 1001 0.1025
## Fst8 passed 1 0.1827
## Fst9 passed 501 0.0645
## Fst10 passed 1 0.7055
## Fst11 passed 1 0.7816
## Fst12 passed 1 0.6587
## Fst13 passed 1 0.1275
## Fst14 passed 1 0.4689
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0400 1.06e-05
## Fst2 passed 0.0156 5.97e-06
## Fst3 passed 0.0208 6.90e-06
## Fst4 passed 0.0186 5.11e-06
## Fst5 passed 0.0234 7.84e-06
## Fst6 passed 0.0238 7.51e-06
## Fst7 passed 0.0102 4.10e-06
## Fst8 passed 0.0710 1.09e-05
## Fst9 passed 0.0173 4.91e-06
## Fst10 passed 0.0157 4.14e-06
## Fst11 passed 0.0505 9.84e-06
## Fst12 passed 0.3482 4.47e-05
## Fst13 passed 0.2261 2.98e-05
## Fst14 passed 0.0378 7.96e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2646.689 2254.656 2259.754 3481.653 4437.453 4573.730 3215.727 3705.813
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3306.769 3245.737 2921.445 4126.123 4355.666 3507.794plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.00000000 1.0000000000 1.000000000
## Lag 10 0.24847485 0.262736801 0.30117751 0.1361696643 0.059408522
## Lag 50 -0.01880866 0.031891540 0.02502463 -0.0079106499 0.004686054
## Lag 100 0.01677790 -0.008283256 0.02281189 -0.0006298801 -0.004081893
## Lag 500 0.01296741 -0.003054415 0.01750167 -0.0110047670 -0.022643676
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.0000000000 1.0000000000
## Lag 10 0.050868392 0.185705990 0.095838175 0.1550192305 0.1795805506
## Lag 50 -0.005768355 -0.005642589 -0.007953327 0.0169562307 0.0002919287
## Lag 100 0.008025088 -0.014638226 0.009838974 0.0004442406 -0.0247072424
## Lag 500 -0.005891639 -0.005208134 0.009309790 0.0058092898 0.0142507410
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.19589327 0.095557353 0.068671879 0.175198841
## Lag 50 0.01160236 0.016612233 0.008869372 0.023110338
## Lag 100 0.00241594 0.021772057 0.008850768 -0.011902869
## Lag 500 0.01472459 0.007206443 -0.010452274 -0.008091698levelplot(t(autocorr.diag(chain)))
plot(chain)
2R temp
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.512
## Fst2 passed 1 0.131
## Fst3 passed 1 0.106
## Fst4 passed 1 0.200
## Fst5 passed 1 0.611
## Fst6 passed 1 0.747
## Fst7 passed 1 0.436
## Fst8 passed 1 0.528
## Fst9 passed 1 0.440
## Fst10 passed 1 0.724
## Fst11 passed 1501 0.194
## Fst12 passed 1501 0.107
## Fst13 passed 1 0.457
## Fst14 passed 1 0.303
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0551 6.39e-06
## Fst2 passed 0.0245 3.77e-06
## Fst3 passed 0.0369 4.40e-06
## Fst4 passed 0.0294 4.02e-06
## Fst5 passed 0.0345 6.38e-06
## Fst6 passed 0.0291 5.69e-06
## Fst7 passed 0.0450 4.71e-06
## Fst8 passed 0.0550 5.08e-06
## Fst9 passed 0.0195 2.90e-06
## Fst10 passed 0.0220 3.04e-06
## Fst11 passed 0.0576 7.04e-06
## Fst12 passed 0.4600 3.02e-05
## Fst13 passed 0.1145 1.07e-05
## Fst14 passed 0.0290 3.62e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3147.592 3032.482 3637.943 3520.779 3855.624 3867.232 3878.774 3543.014
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3047.562 3171.897 3156.707 4320.713 4009.916 3657.050plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.00000000
## Lag 10 0.162016597 0.188756270 0.205311851 0.173407605 0.12902914
## Lag 50 0.011317061 0.032577714 0.007260828 0.021289417 0.01974620
## Lag 100 -0.013950648 0.006557931 -0.029392754 0.029723675 -0.01064528
## Lag 500 -0.009077435 -0.001422646 -0.009115234 -0.004990869 -0.02831390
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.00000000 1.000000e+00 1.000000000 1.000000000
## Lag 10 0.12755090 0.12608475 1.467368e-01 0.212852113 0.223516040
## Lag 50 0.01034468 -0.01252907 -1.190971e-02 0.007647009 0.024294209
## Lag 100 -0.01039281 -0.00161087 2.179456e-02 -0.001098868 -0.002648697
## Lag 500 -0.01365207 0.03881780 6.817774e-05 0.005740903 0.012263952
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.225795035 0.093451554 0.0794489309 0.108732888
## Lag 50 -0.014756946 0.003534171 -0.0007704787 -0.001769024
## Lag 100 0.021502898 -0.022624588 0.0360885518 -0.008080128
## Lag 500 0.001925811 -0.000765012 0.0070347195 0.023141724levelplot(t(autocorr.diag(chain)))
plot(chain)
2L temp
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.252
## Fst2 passed 1 0.483
## Fst3 passed 1 0.695
## Fst4 passed 1 0.561
## Fst5 passed 1 0.589
## Fst6 passed 1 0.599
## Fst7 passed 1 0.914
## Fst8 passed 1 0.413
## Fst9 passed 1 0.203
## Fst10 passed 1 0.793
## Fst11 passed 1 0.614
## Fst12 passed 1 0.139
## Fst13 passed 1 0.749
## Fst14 passed 1 0.186
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0987 1.28e-05
## Fst2 passed 0.0803 1.09e-05
## Fst3 passed 0.0807 1.11e-05
## Fst4 passed 0.0360 5.89e-06
## Fst5 passed 0.0263 7.29e-06
## Fst6 passed 0.0323 7.18e-06
## Fst7 passed 0.0355 5.44e-06
## Fst8 passed 0.0548 6.35e-06
## Fst9 passed 0.0132 3.26e-06
## Fst10 passed 0.0400 6.46e-06
## Fst11 passed 0.0655 8.03e-06
## Fst12 passed 0.4349 3.12e-05
## Fst13 passed 0.1597 1.83e-05
## Fst14 passed 0.0450 6.66e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3004.487 3079.618 2825.089 3353.079 3308.912 3842.727 3206.397 3563.200
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2445.859 2527.364 2987.745 4255.227 3112.026 3019.459plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.198233042 0.218869974 0.234386224 0.19697110 0.172292592
## Lag 50 0.020841610 0.006814003 0.003999675 -0.02417961 -0.006923424
## Lag 100 0.003177167 0.010716036 -0.021364061 0.02086044 0.011375946
## Lag 500 0.009543020 -0.003226655 0.007262111 -0.01339249 0.012680167
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.1306762582 0.186542593 0.167593258 0.302500201 0.248775291
## Lag 50 -0.0187893519 0.009638212 0.009734679 0.011304649 0.034681577
## Lag 100 -0.0152534507 0.022995285 -0.004214589 0.007197374 -0.019834954
## Lag 500 0.0002226343 0.015299374 0.025396423 0.011665819 -0.006464729
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.0000000000 1.000000000 1.0000000000
## Lag 10 0.194434976 0.0802717949 0.163286069 0.1810286584
## Lag 50 0.007495038 0.0060742824 0.032511141 0.0115024485
## Lag 100 0.024054627 -0.0007071935 -0.031589714 -0.0009281255
## Lag 500 0.000458376 -0.0056029174 0.009730656 -0.0095717329levelplot(t(autocorr.diag(chain)))
plot(chain)
3R temp
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.7912
## Fst2 passed 1 0.0926
## Fst3 passed 1 0.2605
## Fst4 passed 1 0.2424
## Fst5 passed 1 0.0605
## Fst6 passed 1 0.2854
## Fst7 passed 1 0.5350
## Fst8 passed 1 0.5128
## Fst9 passed 1 0.4396
## Fst10 passed 1 0.4969
## Fst11 passed 1001 0.0512
## Fst12 passed 1 0.6047
## Fst13 passed 1 0.6322
## Fst14 passed 1 0.2153
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.01747 3.29e-06
## Fst2 passed 0.00548 1.62e-06
## Fst3 passed 0.00911 2.24e-06
## Fst4 passed 0.01690 2.86e-06
## Fst5 passed 0.02159 5.11e-06
## Fst6 passed 0.02049 4.98e-06
## Fst7 passed 0.01927 3.01e-06
## Fst8 passed 0.05708 5.27e-06
## Fst9 passed 0.00964 2.00e-06
## Fst10 passed 0.01025 1.79e-06
## Fst11 passed 0.03520 4.59e-06
## Fst12 passed 0.26936 2.27e-05
## Fst13 passed 0.08160 8.84e-06
## Fst14 passed 0.01922 2.99e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3072.853 3741.941 3030.960 3934.210 4139.249 4007.662 3551.750 4009.870
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2843.208 3390.236 4101.686 4277.894 3859.239 3492.174plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.00000000 1.00000000 1.000000000 1.0000000000
## Lag 10 0.195851314 0.16682696 0.21619896 0.119095949 0.0939835255
## Lag 50 0.004531160 0.02180416 0.01377274 0.002039917 -0.0062985118
## Lag 100 0.011454137 -0.04237385 -0.05194721 0.001826051 0.0002917281
## Lag 500 -0.003774316 -0.01411793 -0.01010821 0.001678955 0.0026022636
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.0000000000 1.0000000000 1.00000000
## Lag 10 0.0681918015 0.146083699 0.1096962846 0.1976099152 0.19502127
## Lag 50 0.0001229178 0.010113647 -0.0001008117 0.0124014381 -0.01164943
## Lag 100 -0.0198575072 0.002378411 0.0021872289 -0.0001017405 -0.02146946
## Lag 500 -0.0041192153 0.002940570 -0.0013853196 -0.0063093198 0.01047925
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.1451900116 0.077631948 0.0878510318 0.142727856
## Lag 50 0.0003551114 0.003535068 -0.0122409057 0.002405534
## Lag 100 0.0060063908 0.013785207 0.0005460158 0.006498615
## Lag 500 0.0036933326 -0.002256197 0.0052509239 0.003796013levelplot(t(autocorr.diag(chain)))
plot(chain)
3L temp
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.1247
## Fst2 passed 1 0.1992
## Fst3 passed 1 0.7122
## Fst4 passed 1 0.6000
## Fst5 passed 1 0.0811
## Fst6 passed 1 0.8510
## Fst7 passed 1 0.3267
## Fst8 passed 1 0.8530
## Fst9 passed 1 0.9213
## Fst10 passed 1 0.5542
## Fst11 passed 1 0.9109
## Fst12 passed 1 0.7381
## Fst13 passed 1 0.2924
## Fst14 passed 1 0.3904
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.01959 3.72e-06
## Fst2 passed 0.00621 2.16e-06
## Fst3 passed 0.01071 2.66e-06
## Fst4 passed 0.01299 2.79e-06
## Fst5 passed 0.01445 5.09e-06
## Fst6 passed 0.01364 5.00e-06
## Fst7 passed 0.01936 3.55e-06
## Fst8 passed 0.05308 5.76e-06
## Fst9 passed 0.00743 1.88e-06
## Fst10 passed 0.00747 2.01e-06
## Fst11 passed 0.03452 4.47e-06
## Fst12 passed 0.25525 2.55e-05
## Fst13 passed 0.08569 1.03e-05
## Fst14 passed 0.01828 3.11e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3667.123 3310.481 3291.176 4194.588 4288.404 4141.828 3404.164 4147.302
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3159.629 2687.834 3734.275 4308.080 4060.241 4076.622plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.00000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.153590112 0.16092346 0.18115400 0.135210256 0.055558992
## Lag 50 0.010503836 0.01261251 -0.02023669 -0.029269439 0.003938625
## Lag 100 0.012514724 -0.02219323 -0.02360201 0.001898819 0.008062668
## Lag 500 0.006570454 -0.01706376 -0.03475784 -0.006194198 0.009411560
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.067926758 0.163776175 0.108767979 0.184321721 0.219407008
## Lag 50 0.012069505 0.008022964 0.007227502 0.003890879 0.023527835
## Lag 100 -0.005448105 -0.009787215 -0.017150126 0.001847150 -0.014093389
## Lag 500 -0.027855815 -0.003193722 -0.022414166 -0.023454967 0.003191097
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.1447188161 0.074136460 0.1060089243 0.121872649
## Lag 50 -0.0008976264 0.009439436 0.0193305753 0.009724819
## Lag 100 0.0103737949 0.012650380 0.0004001628 -0.016618758
## Lag 500 0.0135648573 0.006332512 -0.0160715556 -0.014444973levelplot(t(autocorr.diag(chain)))
plot(chain)
X temp
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.4216
## Fst2 passed 1 0.7876
## Fst3 passed 1 0.3875
## Fst4 passed 1 0.1636
## Fst5 passed 1 0.1673
## Fst6 passed 1 0.1126
## Fst7 passed 1 0.0639
## Fst8 passed 1 0.3982
## Fst9 passed 1 0.8223
## Fst10 passed 1 0.0533
## Fst11 passed 1 0.1277
## Fst12 passed 1 0.6423
## Fst13 passed 1 0.7304
## Fst14 passed 1 0.0731
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0275 8.69e-06
## Fst2 passed 0.0114 4.39e-06
## Fst3 passed 0.0155 5.72e-06
## Fst4 passed 0.0129 4.44e-06
## Fst5 passed 0.0207 7.64e-06
## Fst6 passed 0.0234 8.97e-06
## Fst7 passed 0.0124 3.96e-06
## Fst8 passed 0.0644 9.84e-06
## Fst9 passed 0.0125 3.41e-06
## Fst10 passed 0.0118 3.56e-06
## Fst11 passed 0.0361 8.31e-06
## Fst12 passed 0.3864 4.58e-05
## Fst13 passed 0.2267 3.11e-05
## Fst14 passed 0.0487 9.03e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2147.770 2594.108 2067.092 3197.879 4294.216 3981.507 3550.924 3854.050
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3484.336 2882.584 2643.258 4073.167 4078.991 3757.505plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.260223983 0.230402803 0.28572674 0.194043339 0.075739116
## Lag 50 0.052569252 0.017208106 0.05605234 -0.009588056 -0.001224715
## Lag 100 -0.004981639 -0.003575993 0.02212055 -0.015511812 0.001860171
## Lag 500 -0.013828245 -0.020911686 0.01759601 -0.017340618 -0.021276632
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.0878072635 0.135684714 0.108457071 0.178448875 0.219619551
## Lag 50 -0.0119546241 -0.006136479 0.001609714 0.012264797 0.019802088
## Lag 100 0.0097256926 0.023638165 -0.007415945 0.008854932 0.019480080
## Lag 500 -0.0005856327 -0.039505605 0.007141300 -0.035367941 0.004210166
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.227571101 0.101953016 0.101245988 0.141681650
## Lag 50 0.036457396 -0.023275085 0.013665207 -0.001819869
## Lag 100 0.003876193 -0.003250872 0.007178394 0.014500964
## Lag 500 0.016300111 0.010585012 -0.013174184 0.010089182levelplot(t(autocorr.diag(chain)))
plot(chain)
2R precip
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.1489
## Fst2 passed 1 0.0789
## Fst3 failed NA 0.0065
## Fst4 passed 1 0.6411
## Fst5 passed 1 0.6804
## Fst6 passed 1 0.3438
## Fst7 passed 1 0.5229
## Fst8 passed 1 0.2314
## Fst9 passed 1001 0.0609
## Fst10 passed 1 0.5280
## Fst11 passed 1 0.1765
## Fst12 passed 1 0.1139
## Fst13 passed 1 0.7013
## Fst14 passed 1 0.5901
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0596 7.07e-06
## Fst2 passed 0.0251 4.02e-06
## Fst3 <NA> NA NA
## Fst4 passed 0.0309 3.94e-06
## Fst5 passed 0.0340 6.07e-06
## Fst6 passed 0.0292 5.05e-06
## Fst7 passed 0.0423 4.70e-06
## Fst8 passed 0.0561 5.11e-06
## Fst9 passed 0.0203 3.35e-06
## Fst10 passed 0.0232 3.33e-06
## Fst11 passed 0.0616 5.68e-06
## Fst12 passed 0.4528 2.78e-05
## Fst13 passed 0.1111 1.06e-05
## Fst14 passed 0.0250 3.36e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2900.167 2782.632 2880.959 3841.719 3925.197 4431.342 3502.439 3625.023
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3006.681 2868.145 3490.787 3903.277 3922.602 3813.897plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.186508727 0.222774719 0.2283277938 0.130805132 0.088827207
## Lag 50 0.026588679 0.014936599 0.0271407235 0.007925273 -0.023090505
## Lag 100 -0.003082062 0.004046256 -0.0007477726 -0.014120710 -0.005278209
## Lag 500 0.009450721 0.009399297 -0.0106981526 0.003533678 -0.014557523
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.0000000000
## Lag 10 0.060095117 0.147280952 0.159222157 0.209578475 0.2440846503
## Lag 50 0.014072529 -0.023679778 0.004837739 0.004288884 -0.0005706357
## Lag 100 -0.002659503 0.001112557 0.012911794 0.008608064 -0.0288967689
## Lag 500 0.006154117 0.035265339 0.024450824 0.006690856 0.0055199553
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.157926393 0.099294904 0.1205521981 0.134375439
## Lag 50 0.009547921 0.007786595 0.0038005587 0.004498597
## Lag 100 0.012993270 -0.004707591 0.0087332726 -0.002359100
## Lag 500 -0.008541712 -0.008532294 0.0004562545 -0.008417248levelplot(t(autocorr.diag(chain)))
plot(chain)
2L precip
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.3309
## Fst2 passed 1 0.9388
## Fst3 passed 1 0.6882
## Fst4 passed 1 0.5698
## Fst5 passed 1 0.3761
## Fst6 passed 1 0.0578
## Fst7 passed 1 0.8605
## Fst8 passed 1 0.5517
## Fst9 passed 1 0.2227
## Fst10 passed 1 0.5938
## Fst11 passed 1 0.6640
## Fst12 passed 1 0.7841
## Fst13 passed 1 0.2328
## Fst14 passed 1 0.2760
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.1190 1.38e-05
## Fst2 passed 0.0961 1.12e-05
## Fst3 passed 0.0951 1.12e-05
## Fst4 passed 0.0391 6.07e-06
## Fst5 passed 0.0244 6.53e-06
## Fst6 passed 0.0294 7.04e-06
## Fst7 passed 0.0330 5.19e-06
## Fst8 passed 0.0574 6.35e-06
## Fst9 passed 0.0145 3.53e-06
## Fst10 passed 0.0454 6.53e-06
## Fst11 passed 0.0785 8.32e-06
## Fst12 passed 0.4332 3.28e-05
## Fst13 passed 0.1534 1.68e-05
## Fst14 passed 0.0372 5.94e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3284.625 3287.983 3237.938 3397.167 4013.007 3552.448 3181.907 3729.855
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2345.829 2980.152 3475.078 4101.334 3639.252 3152.176plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000e+00 1.000000000 1.000000000 1.000000000 1.00000000
## Lag 10 1.766333e-01 0.180277678 0.170516397 0.170792038 0.10930993
## Lag 50 -1.036846e-03 0.001269064 -0.011910251 -0.031010238 -0.00847705
## Lag 100 -5.595001e-06 -0.016115596 -0.015585339 0.034103682 0.01643825
## Lag 500 -1.120024e-02 -0.016526486 0.005888039 -0.006269067 -0.01967393
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.1239504475 0.222018705 0.120381406 0.280358795 0.205099531
## Lag 50 -0.0007255508 -0.007621358 0.010752478 0.002793641 -0.005483769
## Lag 100 -0.0135694859 -0.004082289 -0.002321631 0.001769047 0.002692973
## Lag 500 0.0147137599 -0.004804574 -0.023755424 -0.003357714 -0.015966165
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.14476195 0.098541927 0.157312454 0.226476621
## Lag 50 0.01749906 0.001334517 -0.015407404 0.008753364
## Lag 100 -0.01605734 -0.010869439 -0.006536633 0.026573469
## Lag 500 -0.01355492 0.017142958 -0.018289998 0.009261979levelplot(t(autocorr.diag(chain)))
plot(chain)
3R precip
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.836
## Fst2 passed 1 0.728
## Fst3 passed 1 0.518
## Fst4 passed 1 0.961
## Fst5 passed 1 0.602
## Fst6 passed 1 0.255
## Fst7 passed 1 0.500
## Fst8 passed 1 0.238
## Fst9 passed 1 0.453
## Fst10 passed 1 0.656
## Fst11 passed 1 0.866
## Fst12 passed 1 0.643
## Fst13 passed 1 0.918
## Fst14 passed 1 0.173
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02006 3.66e-06
## Fst2 passed 0.00593 2.13e-06
## Fst3 passed 0.00997 2.39e-06
## Fst4 passed 0.01888 3.22e-06
## Fst5 passed 0.02142 5.01e-06
## Fst6 passed 0.02034 4.46e-06
## Fst7 passed 0.01745 2.82e-06
## Fst8 passed 0.05851 5.73e-06
## Fst9 passed 0.01077 1.96e-06
## Fst10 passed 0.01167 2.22e-06
## Fst11 passed 0.03921 4.11e-06
## Fst12 passed 0.26340 2.28e-05
## Fst13 passed 0.07949 8.65e-06
## Fst14 passed 0.01668 2.82e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3093.838 2693.221 2891.973 3287.499 4226.414 4672.749 3476.363 3749.288
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3352.838 2777.592 3667.672 4178.734 3957.035 3602.131plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.00000000 1.0000000000 1.000000000 1.00000000
## Lag 10 0.185221876 0.22442959 0.2183894331 0.153978352 0.08364600
## Lag 50 0.024299614 0.01200389 0.0376251446 0.001627516 0.02749681
## Lag 100 -0.013169613 -0.02520475 -0.0193488627 0.003176575 -0.01430959
## Lag 500 -0.002341593 -0.01137294 -0.0006074088 0.032586827 -0.01631854
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.0000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.0573412424 0.1532439733 0.11592008 0.182017089 0.226338693
## Lag 50 -0.0329665720 0.0056625750 0.01900974 -0.001877686 0.035900900
## Lag 100 -0.0107043120 -0.0028384123 0.02302985 -0.014832551 0.026540401
## Lag 500 -0.0003529382 0.0004135391 0.00509871 0.015635906 -0.007443639
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.153517005 0.089276405 0.076428766 0.162307889
## Lag 50 0.014180211 -0.011086618 0.020448143 0.015135153
## Lag 100 -0.021009544 -0.004781642 0.016415723 0.003967045
## Lag 500 -0.006890984 -0.015677964 0.009459596 0.007700519levelplot(t(autocorr.diag(chain)))
plot(chain)
3L precip
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.171
## Fst2 passed 1 0.181
## Fst3 passed 1 0.186
## Fst4 passed 1 0.494
## Fst5 passed 501 0.200
## Fst6 passed 1 0.458
## Fst7 passed 1 0.315
## Fst8 passed 1 0.541
## Fst9 passed 1 0.171
## Fst10 passed 1 0.143
## Fst11 passed 1 0.119
## Fst12 passed 1 0.816
## Fst13 passed 1 0.450
## Fst14 passed 1 0.736
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02207 4.26e-06
## Fst2 passed 0.00685 2.63e-06
## Fst3 passed 0.01177 2.98e-06
## Fst4 passed 0.01413 3.06e-06
## Fst5 passed 0.01425 5.09e-06
## Fst6 passed 0.01354 4.84e-06
## Fst7 passed 0.01780 3.21e-06
## Fst8 passed 0.05416 5.74e-06
## Fst9 passed 0.00802 2.08e-06
## Fst10 passed 0.00816 1.97e-06
## Fst11 passed 0.03743 4.79e-06
## Fst12 passed 0.25212 2.53e-05
## Fst13 passed 0.08408 9.87e-06
## Fst14 passed 0.01641 3.19e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3355.997 2509.113 2914.500 3606.196 4556.047 4045.471 3687.627 4088.689
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3060.314 3166.897 3539.284 4469.910 4339.374 3735.076plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.00000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.15616725 0.20657693 0.209479211 0.1013654638 0.046258126
## Lag 50 0.01829202 0.03286747 0.031088089 0.0027221068 0.002747186
## Lag 100 0.02682659 -0.02756767 -0.001650494 -0.0008054601 -0.009710605
## Lag 500 -0.02291298 -0.00260386 -0.001794493 0.0154879287 0.009576140
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.05673575 0.150866866 0.114406604 0.2104475237 0.186960009
## Lag 50 0.01508705 -0.018845235 -0.006579446 -0.0065141284 0.006879469
## Lag 100 -0.02424429 0.008579265 0.034956354 -0.0001781693 -0.011529425
## Lag 500 0.00757516 0.028418991 -0.003449054 -0.0088582346 -0.005266466
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.143429990 0.055776790 0.07053657 0.144613913
## Lag 50 0.009178059 -0.011219973 0.01051711 0.004026944
## Lag 100 0.009185220 -0.020327791 -0.01951898 0.021894423
## Lag 500 -0.021234655 -0.001247939 -0.03054100 -0.006748757levelplot(t(autocorr.diag(chain)))
plot(chain)
X precip
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.8692
## Fst2 passed 1 0.2105
## Fst3 passed 1 0.6779
## Fst4 passed 1 0.0763
## Fst5 passed 1 0.9235
## Fst6 passed 1 0.3293
## Fst7 passed 1 0.6249
## Fst8 passed 1 0.6401
## Fst9 passed 1 0.3392
## Fst10 passed 1 0.4518
## Fst11 passed 1 0.5024
## Fst12 passed 1 0.3065
## Fst13 passed 1 0.3790
## Fst14 passed 1 0.3315
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.03848 1.01e-05
## Fst2 passed 0.01593 5.78e-06
## Fst3 passed 0.02137 6.69e-06
## Fst4 passed 0.01918 4.90e-06
## Fst5 passed 0.02252 8.13e-06
## Fst6 passed 0.02327 7.91e-06
## Fst7 passed 0.00959 3.44e-06
## Fst8 passed 0.06953 1.07e-05
## Fst9 passed 0.01684 4.20e-06
## Fst10 passed 0.01691 4.19e-06
## Fst11 passed 0.04885 9.54e-06
## Fst12 passed 0.34245 4.65e-05
## Fst13 passed 0.22117 3.16e-05
## Fst14 passed 0.03725 8.08e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2531.963 2342.168 2324.951 3975.034 3849.335 4092.738 3566.071 3670.116
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3593.416 3477.464 2955.756 4052.768 3879.510 3516.342plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.246548139 0.256448354 0.255991229 0.114004436 0.069603239
## Lag 50 0.035495356 0.013919229 0.041415868 0.017178758 0.007204155
## Lag 100 -0.003505592 0.005144498 -0.000388012 -0.007104983 -0.007348997
## Lag 500 -0.026938095 0.007829937 -0.004074389 0.002623357 -0.016379442
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.089631359 0.167201936 0.114054598 0.16348689 0.179404505
## Lag 50 -0.004999225 -0.012545387 0.002990064 0.01850357 0.011309686
## Lag 100 0.022416478 0.022707686 -0.009676859 0.02520612 -0.004892306
## Lag 500 0.009792738 0.002025453 0.007302044 -0.03417012 0.016233532
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.197376784 0.104436694 0.089460856 0.174019015
## Lag 50 0.007540276 -0.004457085 0.023076191 -0.004640746
## Lag 100 -0.001933773 0.017538614 0.002879939 0.002403782
## Lag 500 -0.025780900 0.016134327 -0.013668942 0.013403557levelplot(t(autocorr.diag(chain)))
plot(chain)
2R rand
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.6574
## Fst2 passed 1 0.9453
## Fst3 passed 1 0.3110
## Fst4 passed 1 0.6462
## Fst5 passed 1 0.7178
## Fst6 passed 1 0.5493
## Fst7 passed 1 0.5725
## Fst8 passed 1 0.6989
## Fst9 passed 1 0.0984
## Fst10 passed 1 0.0605
## Fst11 passed 1 0.1486
## Fst12 passed 1 0.3158
## Fst13 passed 1 0.4384
## Fst14 passed 1001 0.0640
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0581 6.69e-06
## Fst2 passed 0.0238 3.55e-06
## Fst3 passed 0.0378 4.92e-06
## Fst4 passed 0.0290 3.92e-06
## Fst5 passed 0.0320 5.72e-06
## Fst6 passed 0.0303 5.68e-06
## Fst7 passed 0.0423 4.58e-06
## Fst8 passed 0.0555 5.16e-06
## Fst9 passed 0.0192 2.95e-06
## Fst10 passed 0.0234 3.35e-06
## Fst11 passed 0.0576 6.05e-06
## Fst12 passed 0.4527 2.65e-05
## Fst13 passed 0.1120 1.03e-05
## Fst14 passed 0.0280 3.82e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3188.282 3389.091 3129.626 3651.796 4275.484 4031.801 3746.300 3480.751
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2925.427 2941.609 2929.625 4119.397 4166.192 3883.760plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.00000000
## Lag 10 0.184714663 0.191831556 0.210721507 0.15732321 0.07791202
## Lag 50 0.027215369 0.012065056 0.026753678 -0.01808314 -0.02350385
## Lag 100 0.002306636 0.005907411 -0.022273182 0.01664383 0.01283224
## Lag 500 -0.015786914 -0.015651730 0.001208656 -0.01252487 0.01970700
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.072927723 0.143144626 0.178947255 0.225028469 0.235612407
## Lag 50 -0.011136590 -0.024166320 -0.022281594 0.008758923 0.008084121
## Lag 100 -0.008340693 -0.012098104 -0.004644657 -0.011532431 -0.012365571
## Lag 500 -0.009933778 -0.007334487 -0.003486653 -0.004045488 0.004411528
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.00000000 1.0000000000
## Lag 10 0.220242041 0.096365478 0.09076723 0.1254525507
## Lag 50 0.023864054 -0.002768341 -0.02019092 0.0281204942
## Lag 100 -0.035434942 -0.013167522 0.01247540 0.0066609214
## Lag 500 -0.002498027 0.011559640 -0.02193530 0.0005111049levelplot(t(autocorr.diag(chain)))
plot(chain)
2L rand
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.902
## Fst2 passed 1 0.945
## Fst3 passed 1 0.875
## Fst4 passed 1 0.732
## Fst5 passed 1 0.365
## Fst6 passed 1 0.432
## Fst7 passed 1 0.694
## Fst8 passed 1 0.905
## Fst9 passed 1 0.948
## Fst10 passed 1 0.843
## Fst11 passed 1 0.361
## Fst12 passed 1 0.274
## Fst13 passed 1 0.860
## Fst14 passed 1 0.232
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.1175 1.32e-05
## Fst2 passed 0.0865 1.19e-05
## Fst3 passed 0.0918 1.08e-05
## Fst4 passed 0.0354 6.10e-06
## Fst5 passed 0.0211 6.63e-06
## Fst6 passed 0.0328 8.12e-06
## Fst7 passed 0.0325 5.17e-06
## Fst8 passed 0.0565 6.21e-06
## Fst9 passed 0.0136 3.52e-06
## Fst10 passed 0.0480 6.89e-06
## Fst11 passed 0.0711 8.72e-06
## Fst12 passed 0.4272 3.15e-05
## Fst13 passed 0.1513 1.75e-05
## Fst14 passed 0.0406 6.17e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3257.721 2959.814 3364.339 3057.826 3514.396 3333.265 3199.952 3856.727
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2217.834 3038.246 2922.385 4287.008 3175.443 3123.072plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.173916955 0.218377369 0.178808977 0.221643577 0.145702968
## Lag 50 0.016586936 0.009386048 0.006737755 0.008179841 -0.011383141
## Lag 100 0.010418862 0.017910051 -0.015865436 -0.012018199 -0.008855598
## Lag 500 0.001915428 0.014655187 -0.005591513 0.007316846 0.019453875
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.139811822 0.161925573 0.128888476 0.312080447 0.209405994
## Lag 50 0.012755358 0.006319802 0.004649574 0.028790475 0.021844110
## Lag 100 -0.009783806 -0.015589399 -0.014265685 -0.007784115 -0.020723768
## Lag 500 -0.009735445 0.020159596 0.016054284 -0.013096760 0.003153665
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.224085616 0.076574181 0.164035323 0.187328545
## Lag 50 0.023776450 0.011721616 0.002945322 -0.008587925
## Lag 100 -0.012021123 -0.005837795 -0.002331670 -0.006443290
## Lag 500 0.007844993 -0.001537194 -0.023219704 -0.010031280levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size3R rand
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.698
## Fst2 passed 1 0.444
## Fst3 passed 1 0.264
## Fst4 passed 1 0.634
## Fst5 passed 1 0.753
## Fst6 passed 1 0.761
## Fst7 passed 1 0.889
## Fst8 passed 1 0.858
## Fst9 passed 1 0.939
## Fst10 passed 1 0.848
## Fst11 passed 1 0.505
## Fst12 passed 1 0.787
## Fst13 passed 1 0.758
## Fst14 passed 1 0.251
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02009 3.93e-06
## Fst2 passed 0.00511 1.85e-06
## Fst3 passed 0.00993 2.44e-06
## Fst4 passed 0.01689 3.03e-06
## Fst5 passed 0.01899 5.10e-06
## Fst6 passed 0.02124 5.20e-06
## Fst7 passed 0.01692 2.71e-06
## Fst8 passed 0.05783 5.50e-06
## Fst9 passed 0.00991 2.22e-06
## Fst10 passed 0.01206 2.36e-06
## Fst11 passed 0.03574 4.24e-06
## Fst12 passed 0.25975 2.29e-05
## Fst13 passed 0.07960 8.35e-06
## Fst14 passed 0.01817 2.73e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2525.655 3217.245 2661.084 3501.064 3802.210 3722.122 3516.279 3877.517
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2483.876 2767.304 3302.869 4117.010 4147.596 3786.155plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.176461018 0.179739994 0.18817068 0.151794806 0.08128458
## Lag 50 0.014582962 0.019316639 0.02394231 0.014730435 0.01378353
## Lag 100 -0.015411410 -0.008330382 0.01545078 0.004048946 -0.01564760
## Lag 500 -0.001237417 -0.006350326 0.01113369 0.017394197 0.01406249
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.072519887 0.139560982 0.126244267 0.21980929 0.238079647
## Lag 50 0.005671132 0.013274500 -0.006651277 0.01945987 -0.016800518
## Lag 100 -0.020089587 0.012840250 0.025278697 0.01296286 0.007643752
## Lag 500 -0.007844849 0.003094601 -0.018783885 0.01928526 0.000521164
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.000000000 1.00000000
## Lag 10 0.1698166332 0.096652607 0.056543984 0.13795805
## Lag 50 -0.0114375766 0.007914173 0.004611181 -0.01411984
## Lag 100 0.0151359079 -0.013874775 -0.024511675 -0.01162457
## Lag 500 -0.0002839298 -0.009079355 0.001426539 -0.01984192levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size3L rand
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.0819
## Fst2 passed 1 0.6922
## Fst3 passed 1 0.2598
## Fst4 passed 1 0.2878
## Fst5 passed 1 0.3931
## Fst6 passed 1 0.0884
## Fst7 passed 1 0.7681
## Fst8 passed 1 0.9699
## Fst9 passed 1 0.2433
## Fst10 passed 1 0.5995
## Fst11 passed 1 0.3980
## Fst12 passed 1 0.3205
## Fst13 passed 1 0.7607
## Fst14 passed 1 0.6768
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02197 4.34e-06
## Fst2 passed 0.00598 2.16e-06
## Fst3 passed 0.01153 2.84e-06
## Fst4 passed 0.01285 2.96e-06
## Fst5 passed 0.01248 4.83e-06
## Fst6 passed 0.01388 4.74e-06
## Fst7 passed 0.01724 3.30e-06
## Fst8 passed 0.05360 5.70e-06
## Fst9 passed 0.00751 1.93e-06
## Fst10 passed 0.00833 2.00e-06
## Fst11 passed 0.03486 4.77e-06
## Fst12 passed 0.24880 2.55e-05
## Fst13 passed 0.08400 9.74e-06
## Fst14 passed 0.01750 3.10e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2964.556 3358.155 3102.710 3716.205 4275.984 4646.524 3211.386 4159.778
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3143.329 3183.884 3299.012 4151.293 4359.127 3682.360plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.196676938 0.172814168 0.17278142 0.147092549 0.063514450
## Lag 50 0.015562083 0.011339276 0.01131875 -0.009498047 0.017808546
## Lag 100 -0.007951344 -0.001966161 -0.01630570 -0.007517068 -0.031511698
## Lag 500 0.002443305 0.015352459 0.03328917 0.003436301 -0.003281559
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.00000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.064519844 0.16845074 0.091531085 0.18228668 0.185856547
## Lag 50 0.002178332 -0.02166698 0.000806102 0.01051918 0.016192437
## Lag 100 -0.004012611 -0.01096253 -0.010769341 0.02633049 -0.005121142
## Lag 500 -0.019631251 -0.01539135 -0.009434356 0.01495484 0.020505785
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.163373180 0.092543436 0.068276640 0.130108662
## Lag 50 0.009666382 0.018854557 -0.008055092 -0.001487244
## Lag 100 -0.013206618 0.019240878 -0.006243558 -0.017340689
## Lag 500 0.002262949 0.005063484 -0.005790405 0.008740231levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file sizeX rand
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.4130
## Fst2 passed 1 0.7538
## Fst3 passed 1 0.7695
## Fst4 passed 1 0.8422
## Fst5 passed 1 0.8118
## Fst6 passed 1 0.9362
## Fst7 passed 1 0.0611
## Fst8 passed 1 0.5466
## Fst9 passed 1 0.5979
## Fst10 passed 1 0.6868
## Fst11 passed 1 0.1710
## Fst12 passed 1 0.4383
## Fst13 passed 1 0.9111
## Fst14 passed 1 0.4081
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.04549 1.12e-05
## Fst2 passed 0.01036 5.68e-06
## Fst3 passed 0.02012 6.37e-06
## Fst4 passed 0.01262 5.79e-06
## Fst5 passed 0.01360 8.01e-06
## Fst6 passed 0.02573 9.82e-06
## Fst7 passed 0.00778 3.62e-06
## Fst8 passed 0.06754 1.02e-05
## Fst9 passed 0.01345 4.54e-06
## Fst10 passed 0.02235 7.38e-06
## Fst11 passed 0.03803 8.72e-06
## Fst12 passed 0.33025 5.23e-05
## Fst13 passed 0.21554 3.15e-05
## Fst14 passed 0.03949 7.62e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2435.216 1817.289 2267.104 2030.316 2817.245 3667.422 2762.269 4003.063
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2277.167 2165.847 2566.946 3135.451 3742.462 3684.990plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.00000000 1.00000000 1.000000000 1.0000000000
## Lag 10 0.23848071 0.32619571 0.27337901 0.232617735 0.1576148586
## Lag 50 0.03670575 0.06808487 0.04082410 0.060792825 0.0358401141
## Lag 100 0.02312252 0.00943782 0.01247414 0.030748092 0.0002886658
## Lag 500 -0.01071359 -0.01240479 -0.03799974 -0.004012279 -0.0020175678
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.10129959 0.196922426 0.11053544 0.233462457 0.29129530
## Lag 50 0.01421689 -0.008022926 0.01753971 0.064882453 0.05023543
## Lag 100 -0.02237282 0.002406781 -0.00415637 0.014518729 -0.02252940
## Lag 500 -0.02284059 0.008862334 -0.02483857 0.001061008 0.02246602
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.0000000000 1.0000000000 1.0000000000
## Lag 10 0.240758663 0.1319666496 0.1139277166 0.1512162610
## Lag 50 0.016270535 0.0008020016 0.0003018905 0.0180409642
## Lag 100 -0.005932871 0.0123687483 0.0019310575 0.0032779431
## Lag 500 0.004242687 -0.0127916319 0.0037789503 -0.0009754332levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size2R corr
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.1567
## Fst2 passed 1 0.2439
## Fst3 passed 1 0.1189
## Fst4 passed 1 0.2483
## Fst5 passed 1 0.3646
## Fst6 passed 1 0.5694
## Fst7 passed 1 0.1652
## Fst8 passed 1 0.4147
## Fst9 passed 2001 0.0887
## Fst10 passed 1 0.2128
## Fst11 passed 1 0.1100
## Fst12 passed 1 0.7656
## Fst13 passed 1 0.3387
## Fst14 passed 1 0.2939
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0588 6.23e-06
## Fst2 passed 0.0250 3.60e-06
## Fst3 passed 0.0377 4.90e-06
## Fst4 passed 0.0300 4.00e-06
## Fst5 passed 0.0351 5.89e-06
## Fst6 passed 0.0285 5.39e-06
## Fst7 passed 0.0442 4.64e-06
## Fst8 passed 0.0566 4.99e-06
## Fst9 passed 0.0202 4.09e-06
## Fst10 passed 0.0231 3.35e-06
## Fst11 passed 0.0593 5.64e-06
## Fst12 passed 0.4503 2.73e-05
## Fst13 passed 0.1098 1.03e-05
## Fst14 passed 0.0288 3.73e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3555.914 3489.981 3078.990 3794.559 4181.377 4041.699 3797.493 3752.406
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2771.708 2754.915 3380.949 4043.280 4108.191 3490.012plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.0000000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.168587874 0.1776651791 0.1958047181 0.136870352 0.088962805
## Lag 50 0.001240395 -0.0008948971 -0.0007362333 -0.003955262 -0.009919434
## Lag 100 0.009627360 0.0118690405 0.0267553704 -0.016893761 0.000891851
## Lag 500 0.024846297 -0.0230061371 -0.0078393392 0.004196835 0.011274971
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.1057890229 0.136491167 0.142346911 0.19956141 0.242810666
## Lag 50 0.0017418407 -0.003290712 -0.005268878 0.01071085 0.023723491
## Lag 100 -0.0005252987 -0.001370500 0.002806994 0.01263903 -0.014119630
## Lag 500 -0.0177391215 0.001385723 -0.016114454 0.01760198 0.009257047
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.170927523 0.105595664 0.097714652 0.138572118
## Lag 50 0.020972656 0.009047115 0.007845143 -0.014795044
## Lag 100 -0.001332428 -0.004845484 -0.014368449 0.005944289
## Lag 500 0.010890092 -0.012095286 0.010810719 0.008017509levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size2L corr
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.239
## Fst2 passed 1 0.743
## Fst3 passed 1 0.779
## Fst4 passed 1 0.780
## Fst5 passed 1 0.287
## Fst6 passed 1 0.452
## Fst7 passed 1 0.520
## Fst8 passed 1 0.659
## Fst9 passed 1 0.418
## Fst10 passed 1 0.178
## Fst11 passed 1 0.388
## Fst12 passed 1 0.784
## Fst13 passed 1 0.611
## Fst14 passed 1 0.705
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.1212 1.37e-05
## Fst2 passed 0.0905 1.08e-05
## Fst3 passed 0.0897 1.02e-05
## Fst4 passed 0.0373 6.40e-06
## Fst5 passed 0.0262 7.65e-06
## Fst6 passed 0.0290 6.88e-06
## Fst7 passed 0.0347 5.09e-06
## Fst8 passed 0.0578 6.41e-06
## Fst9 passed 0.0150 3.45e-06
## Fst10 passed 0.0482 7.43e-06
## Fst11 passed 0.0721 7.97e-06
## Fst12 passed 0.4151 3.25e-05
## Fst13 passed 0.1417 1.62e-05
## Fst14 passed 0.0434 6.22e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3226.260 3420.290 3694.200 3046.744 3090.305 3678.611 3594.275 3669.967
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2500.083 2510.460 3348.354 4039.824 3669.919 3246.245plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.170934394 0.164179861 0.18056097 0.177101555 0.14523007
## Lag 50 -0.002521939 0.003936389 0.01906813 0.015088535 0.01485925
## Lag 100 -0.010875784 -0.009038200 -0.03276034 -0.002962940 -0.01442010
## Lag 500 0.013507290 0.021517275 -0.01285042 -0.003947699 0.03271510
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.1237973829 0.163370610 0.0977194089 0.307213569 0.218251754
## Lag 50 0.0385630512 0.017147150 0.0002478026 -0.005699051 0.041030438
## Lag 100 -0.0281433267 0.006047292 0.0079196634 -0.011739857 -0.022107570
## Lag 500 0.0005123491 0.004855602 0.0021764374 0.017230558 0.001389491
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.17619022 0.106018412 0.153217960 0.212482138
## Lag 50 -0.01410351 -0.006182297 -0.005255178 -0.012968481
## Lag 100 0.01366072 -0.012085173 0.016417243 0.009675629
## Lag 500 0.01322821 0.016207286 -0.001168821 0.000782476levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size3R corr
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.2127
## Fst2 passed 1 0.4671
## Fst3 passed 1 0.9548
## Fst4 passed 1 0.7915
## Fst5 passed 1 0.8479
## Fst6 passed 1 0.8126
## Fst7 passed 501 0.0632
## Fst8 passed 1 0.3070
## Fst9 passed 1 0.0660
## Fst10 passed 1 0.6954
## Fst11 passed 1 0.5585
## Fst12 passed 1 0.3304
## Fst13 passed 501 0.0894
## Fst14 passed 1 0.4378
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02006 3.62e-06
## Fst2 passed 0.00597 1.97e-06
## Fst3 passed 0.00996 2.36e-06
## Fst4 passed 0.01813 2.91e-06
## Fst5 passed 0.02195 4.99e-06
## Fst6 passed 0.01989 4.90e-06
## Fst7 passed 0.01849 2.94e-06
## Fst8 passed 0.05889 5.65e-06
## Fst9 passed 0.01058 2.11e-06
## Fst10 passed 0.01148 2.12e-06
## Fst11 passed 0.03749 4.00e-06
## Fst12 passed 0.25806 2.38e-05
## Fst13 passed 0.07877 9.06e-06
## Fst14 passed 0.01855 2.87e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3081.572 2932.849 2969.362 4012.032 4317.203 3787.457 3716.384 3670.020
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2821.773 2845.133 3939.303 3949.144 3863.843 3676.380plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000 1.0000000000
## Lag 10 0.19375816 0.167346557 0.207544154 0.109429986 0.0474377754
## Lag 50 0.03370195 0.033972855 -0.001632805 0.009671385 -0.0005616828
## Lag 100 0.01487360 0.009856599 -0.003109365 -0.021745536 -0.0002144461
## Lag 500 0.01180906 -0.009080761 -0.014157115 0.015293470 0.0005588691
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000e+00 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 1.045221e-01 0.147069040 0.119797388 0.198983767 0.239587439
## Lag 50 4.811015e-03 0.001326572 -0.006603822 0.009194855 0.017563612
## Lag 100 -9.855211e-05 0.031640313 0.022895591 -0.002896565 -0.006168659
## Lag 500 -1.802687e-02 -0.003443671 -0.004019166 0.019565233 -0.012704552
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.00000000 1.0000000000 1.000000000
## Lag 10 0.118458166 0.07730739 0.1279820004 0.117988193
## Lag 50 0.018062236 -0.01281953 -0.0121188225 0.010485454
## Lag 100 -0.012906869 0.01055783 -0.0001704971 0.006284584
## Lag 500 -0.006395941 -0.01635416 0.0140383422 -0.010687296levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size3L corr
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.866
## Fst2 passed 1 0.524
## Fst3 passed 1 0.384
## Fst4 passed 1 0.177
## Fst5 passed 1 0.435
## Fst6 passed 1 0.697
## Fst7 passed 1 0.565
## Fst8 passed 1 0.113
## Fst9 passed 1 0.692
## Fst10 passed 1 0.943
## Fst11 passed 1 0.979
## Fst12 passed 1 0.411
## Fst13 passed 1 0.909
## Fst14 passed 1 0.154
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.02212 4.36e-06
## Fst2 passed 0.00671 2.18e-06
## Fst3 passed 0.01154 2.70e-06
## Fst4 passed 0.01365 2.99e-06
## Fst5 passed 0.01445 4.88e-06
## Fst6 passed 0.01326 4.45e-06
## Fst7 passed 0.01860 3.30e-06
## Fst8 passed 0.05435 6.26e-06
## Fst9 passed 0.00795 2.00e-06
## Fst10 passed 0.00809 2.11e-06
## Fst11 passed 0.03603 3.86e-06
## Fst12 passed 0.24694 2.53e-05
## Fst13 passed 0.08336 9.99e-06
## Fst14 passed 0.01787 3.13e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3133.664 3540.840 3486.318 3667.896 4416.309 4602.765 3646.325 3580.975
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3161.739 2811.748 5136.474 4246.270 4267.666 3771.724plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.0000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.176669693 0.1706508603 0.16257734 0.127317362 0.061787912
## Lag 50 0.006717724 -0.0092433436 -0.02778312 -0.014458246 -0.018423721
## Lag 100 0.013742640 0.0108210471 -0.00543404 0.004961475 0.004324964
## Lag 500 -0.009028767 0.0004345302 -0.01489376 0.013593804 -0.009199619
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.046827318 0.156365576 0.116728493 0.192613908 0.236817281
## Lag 50 0.004556912 0.025176859 -0.021603835 0.012821565 -0.002123151
## Lag 100 -0.011279939 -0.015205307 0.002252461 -0.028783365 0.012455142
## Lag 500 0.010375905 0.009786558 -0.019529180 -0.003098394 -0.011591084
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000e+00
## Lag 10 0.14537377 0.081318520 0.078821549 1.398307e-01
## Lag 50 -0.02045055 0.021264384 0.004785627 8.478993e-05
## Lag 100 -0.04199109 -0.008237217 0.018261106 -6.366198e-03
## Lag 500 0.01762633 0.007047980 -0.004627486 -9.497448e-03#levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file sizeX corr
#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.801
## Fst2 passed 1 0.537
## Fst3 passed 1 0.844
## Fst4 passed 1 0.927
## Fst5 passed 1 0.850
## Fst6 passed 1 0.307
## Fst7 passed 1 0.721
## Fst8 passed 1 0.494
## Fst9 passed 1 0.514
## Fst10 passed 1 0.164
## Fst11 passed 1 0.893
## Fst12 passed 1 0.586
## Fst13 passed 1 0.133
## Fst14 passed 1 0.930
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0498 1.35e-05
## Fst2 passed 0.0134 5.07e-06
## Fst3 passed 0.0180 6.27e-06
## Fst4 passed 0.0140 5.06e-06
## Fst5 passed 0.0190 7.47e-06
## Fst6 passed 0.0192 7.10e-06
## Fst7 passed 0.0105 3.54e-06
## Fst8 passed 0.0706 9.77e-06
## Fst9 passed 0.0177 4.63e-06
## Fst10 passed 0.0177 5.04e-06
## Fst11 passed 0.0418 8.49e-06
## Fst12 passed 0.3076 5.03e-05
## Fst13 passed 0.2146 3.29e-05
## Fst14 passed 0.0435 8.75e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2185.957 2552.002 2199.258 2821.450 4084.059 4406.382 3533.534 4401.892
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3293.725 2874.069 3115.245 3510.468 3407.479 3222.053plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.25873801 0.244915071 0.282993880 0.153944842 0.077058807
## Lag 50 0.03310360 0.041129280 0.024821543 0.040250654 -0.006296514
## Lag 100 -0.01400169 -0.040825265 -0.009632776 0.006806845 -0.001445193
## Lag 500 0.02547378 0.003461243 -0.009982131 0.005070720 -0.027701370
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000 1.00000000
## Lag 10 0.062908716 0.1716533007 0.063416399 0.185537830 0.17834904
## Lag 50 0.019762112 0.0008813034 -0.021527492 0.001000342 0.01164169
## Lag 100 -0.006048685 0.0159285506 -0.005635439 -0.016699548 0.01492923
## Lag 500 0.009029047 -0.0010731644 -0.026589240 0.040094760 0.01272903
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.2026575072 0.123638023 0.150450850 0.137074433
## Lag 50 0.0006123029 -0.007167739 0.014285323 0.033191547
## Lag 100 0.0066701308 0.011783190 0.019789047 -0.008118197
## Lag 500 0.0088944235 0.002384724 -0.003072994 -0.016617467levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size